subject:(supervised learning)

1. Lease, Matthew A. Beyond keywords: finding information more accurately and easily using natural language.

Degree: PhD, Computer Science, 2009, Brown University

URL: https://repository.library.brown.edu/studio/item/bdr:206/

► Information retrieval (IR) has become a ubiquitous technology for quickly and easily finding information on a given topic amidst the wealth of digital content available… (more)

▼ Information retrieval (IR) has become a ubiquitous technology for quickly and easily finding information on a given topic amidst the wealth of digital content available today. This dissertation addresses search for written and spoken natural language documents, including news articles, Web pages, and spoken interviews. Effective model estimation is identified as a key problem, and several novel estimation techniques are presented and shown to significantly enhance search accuracy. While search is typically performed via a few carefully chosen keywords, formulating effective keyword queries is often unintuitive and iterative, particularly when seeking complex information. As an alternative to keyword search, this dissertation investigates search using ``natural'' queries, such as questions or sentences a person might naturally articulate in communicating their information need to another person. By moving toward supporting natural queries, the communication burden is shifted from user query formulation to system interpretation of natural language. The challenge in enacting such a shift is enabling automatic IR systems to more effectively cope with natural language. To this end, several new estimation techniques for modeling natural queries are described. In comparison to a maximum likelihood baseline, 15-20% relative improvement in mean average precision (MAP) is demonstrated without use of query expansion. When an IR system discovers or is provided one or more feedback documents exemplifying a user's information need, there is further opportunity to improve search accuracy by exploiting document contents for query expansion. However, since documents typically discuss multiple topics varying in importance and relevance to any information need, the system must again be able to effectively interpret verbose natural language. Consequently, an estimation method for leveraging such documents is presented and shown to yield state-of-the-art search accuracy. Depending on the base model employed, 15-85% relative MAP improvement is achieved. When modeling higher-order lexical features or searching smaller document collections like cultural history archives, sparsity become particularly problematic for estimation. To cope with such sparsity, additional estimation methods are described which yield 5-20% relative improvement in MAP accuracy across varying conditions of query verbosity. Advisors/Committee Members: Charniak, Eugene (director), Johnson, Mark (reader), Allan, James (reader).

Subjects/Keywords: supervised learning

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APA (6^th Edition):

Lease, M. A. (2009). Beyond keywords: finding information more accurately and easily using natural language. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:206/

Chicago Manual of Style (16^th Edition):

Lease, Matthew A. “Beyond keywords: finding information more accurately and easily using natural language.” 2009. Doctoral Dissertation, Brown University. Accessed January 17, 2021. https://repository.library.brown.edu/studio/item/bdr:206/.

MLA Handbook (7^th Edition):

Lease, Matthew A. “Beyond keywords: finding information more accurately and easily using natural language.” 2009. Web. 17 Jan 2021.

Vancouver:

Lease MA. Beyond keywords: finding information more accurately and easily using natural language. [Internet] [Doctoral dissertation]. Brown University; 2009. [cited 2021 Jan 17]. Available from: https://repository.library.brown.edu/studio/item/bdr:206/.

Council of Science Editors:

Lease MA. Beyond keywords: finding information more accurately and easily using natural language. [Doctoral Dissertation]. Brown University; 2009. Available from: https://repository.library.brown.edu/studio/item/bdr:206/

University of Illinois – Chicago

2. Mohammadi, Neshat. Supervised Tensor Learning with Applications.

Degree: 2017, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/22099

► In this thesis, a new supervised tensor learning (STL) approach with application to neuroimages has been studied and implemented. We applied our proposed polynomial kernel-based… (more)

Subjects/Keywords: Supervised Tensor Learning

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APA (6^th Edition):

Mohammadi, N. (2017). Supervised Tensor Learning with Applications. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/22099

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Mohammadi, Neshat. “Supervised Tensor Learning with Applications.” 2017. Thesis, University of Illinois – Chicago. Accessed January 17, 2021. http://hdl.handle.net/10027/22099.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Mohammadi, Neshat. “Supervised Tensor Learning with Applications.” 2017. Web. 17 Jan 2021.

Vancouver:

Mohammadi N. Supervised Tensor Learning with Applications. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/10027/22099.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mohammadi N. Supervised Tensor Learning with Applications. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/22099

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oregon State University

3. Hao, Guohua. Revisiting output coding for sequential supervised learning.

Degree: MS, Computer Science, 2009, Oregon State University

URL: http://hdl.handle.net/1957/10897

► Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training… (more)

Subjects/Keywords: ECOC; Supervised learning (Machine learning)

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APA (6^th Edition):

Hao, G. (2009). Revisiting output coding for sequential supervised learning. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10897

Chicago Manual of Style (16^th Edition):

Hao, Guohua. “Revisiting output coding for sequential supervised learning.” 2009. Masters Thesis, Oregon State University. Accessed January 17, 2021. http://hdl.handle.net/1957/10897.

MLA Handbook (7^th Edition):

Hao, Guohua. “Revisiting output coding for sequential supervised learning.” 2009. Web. 17 Jan 2021.

Vancouver:

Hao G. Revisiting output coding for sequential supervised learning. [Internet] [Masters thesis]. Oregon State University; 2009. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/1957/10897.

Council of Science Editors:

Hao G. Revisiting output coding for sequential supervised learning. [Masters Thesis]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/10897

Rutgers University

4. Gazzola, Gianluca. Supervised learning methods for variable importance and regression with uncertainty on dependent data.

Degree: PhD, Operations Research, 2019, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/60158/

►

This dissertation covers a collection of supervised learning methods targeted to data with complex dependence patterns. Part of our work orbits around the concept of… (more)

Subjects/Keywords: Supervised learning (Machine learning)

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APA (6^th Edition):

Gazzola, G. (2019). Supervised learning methods for variable importance and regression with uncertainty on dependent data. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/60158/

Chicago Manual of Style (16^th Edition):

Gazzola, Gianluca. “Supervised learning methods for variable importance and regression with uncertainty on dependent data.” 2019. Doctoral Dissertation, Rutgers University. Accessed January 17, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/60158/.

MLA Handbook (7^th Edition):

Gazzola, Gianluca. “Supervised learning methods for variable importance and regression with uncertainty on dependent data.” 2019. Web. 17 Jan 2021.

Vancouver:

Gazzola G. Supervised learning methods for variable importance and regression with uncertainty on dependent data. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2021 Jan 17]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60158/.

Council of Science Editors:

Gazzola G. Supervised learning methods for variable importance and regression with uncertainty on dependent data. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60158/

5. Aversano, Gianmarco. Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif.

Degree: Docteur es, Combustion, 2019, Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....)

URL: http://www.theses.fr/2019SACLC095

► L’objectif final étant de développer des modèles d’ordre réduit pour les applications de combustion, des techniques d’apprentissage automatique non supervisées et supervisées ont été testées… (more)

▼ L’objectif final étant de développer des modèles d’ordre réduit pour les applications de combustion, des techniques d’apprentissage automatique non supervisées et supervisées ont été testées et combinées dans les travaux de la présente thèse pour l’extraction de caractéristiques et la construction de modèles d’ordre réduit. Ainsi, l’application de techniques pilotées par les données pour la détection des caractéristiques d’ensembles de données de combustion turbulente (simulation numérique directe) a été étudiée sur deux flammes H2 / CO: une évolution spatiale (DNS1) et une jet à évolution temporelle (DNS2). Des méthodes telles que l’analyse en composantes principales (ACP), l’analyse en composantes principales locales (LPCA), la factorisation matricielle non négative (NMF) et les autoencodeurs ont été explorées à cette fin. Il a été démontré que divers facteurs pouvaient affecter les performances de ces méthodes, tels que les critères utilisés pour le centrage et la mise à l’échelle des données d’origine ou le choix du nombre de dimensions dans les approximations de rang inférieur. Un ensemble de lignes directrices a été présenté qui peut aider le processus d’identification de caractéristiques physiques significatives à partir de données de flux réactifs turbulents. Des méthodes de compression de données telles que l’analyse en composantes principales (ACP) et les variations ont été combinées à des méthodes d’interpolation telles que le krigeage, pour la construction de modèles ordonnées à prix réduits et calculables pour la prédiction de l’état d’un système de combustion dans des conditions de fonctionnement inconnues ou des combinaisons de modèles valeurs de paramètre d’entrée. La méthodologie a d’abord été testée pour la prévision des flammes 1D avec un nombre croissant de paramètres d’entrée (rapport d’équivalence, composition du carburant et température d’entrée), avec des variantes de l’approche PCA classique, à savoir PCA contrainte et PCA locale, appliquée aux cas de combustion la première fois en combinaison avec une technique d’interpolation. Les résultats positifs de l’étude ont conduit à l’application de la méthodologie proposée aux flammes 2D avec deux paramètres d’entrée, à savoir la composition du combustible et la vitesse d’entrée, qui ont donné des résultats satisfaisants. Des alternatives aux méthodes non supervisées et supervisées choisies ont également été testées sur les mêmes données 2D. L’utilisation de la factorisation matricielle non négative (FNM) pour l’approximation de bas rang a été étudiée en raison de la capacité de la méthode à représenter des données à valeur positive, ce qui permet de ne pas enfreindre des lois physiques importantes telles que la positivité des fractions de masse d’espèces chimiques et comparée à la PCA. Comme méthodes supervisées alternatives, la combinaison de l’expansion du chaos polynomial (PCE) et du Kriging et l’utilisation de réseaux de neurones artificiels (RNA) ont été testées. Les résultats des travaux susmentionnés ont ouvert la voie au développement d’un… Advisors/Committee Members: Gicquel, Olivier (thesis director), Parente, Alessandro (thesis director).

Subjects/Keywords: Combustion; Unsupervised learning; Supervised learning; Combustion; Unsupervised learning; Supervised learning

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APA (6^th Edition):

Aversano, G. (2019). Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif. (Doctoral Dissertation). Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....). Retrieved from http://www.theses.fr/2019SACLC095

Chicago Manual of Style (16^th Edition):

Aversano, Gianmarco. “Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif.” 2019. Doctoral Dissertation, Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....). Accessed January 17, 2021. http://www.theses.fr/2019SACLC095.

MLA Handbook (7^th Edition):

Aversano, Gianmarco. “Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif.” 2019. Web. 17 Jan 2021.

Vancouver:

Aversano G. Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....); 2019. [cited 2021 Jan 17]. Available from: http://www.theses.fr/2019SACLC095.

Council of Science Editors:

Aversano G. Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....); 2019. Available from: http://www.theses.fr/2019SACLC095

University of Adelaide

6. Shen, Tong. Context Learning and Weakly Supervised Learning for Semantic Segmentation.

Degree: 2018, University of Adelaide

URL: http://hdl.handle.net/2440/120354

► This thesis focuses on one of the fundamental problems in computer vision, semantic segmentation, whose task is to predict a semantic label for each pixel… (more)

▼ This thesis focuses on one of the fundamental problems in computer vision, semantic segmentation, whose task is to predict a semantic label for each pixel of an image. Although semantic segmentation models have been largely improved thanks to the great representative power of deep learning techniques, there are still open questions needed to be discussed. In this thesis, we discuss two problems regarding semantic segmentation, scene consistency and weakly supervised segmentation. In the first part of the thesis, we discuss the issue of scene consistency in semantic segmentation. This issue comes from the fact that trained models sometimes produce noisy and implausible predictions that are not semantically consistent with the scene or context. By explicitly considering scene consistency both locally and globally, we can narrow down the possible categories for each pixel and generate the desired prediction more easily. In the thesis, we address this issue by introducing a dense multi-label module. In general, multi-label classification refers to the task of assigning multiple labels to a given image. We extend the idea to different levels of the image, and assign multiple labels to different regions of the image. Dense multi-label acts as a constraint to encourage scene consistency locally and globally. For dense prediction problems such as semantic segmentation, training a model requires densely annotated data as ground-truth, which involves a great amount of human annotation effort and is very time-consuming. Therefore, it is worth investigating semi- or weakly supervised methods that require much less supervision. Particularly, weakly supervised segmentation refers to training the model using only image-level labels, while semi-supervised segmentation refers to using partially annotated data or a small portion of fully annotated data to train. In the thesis, two weakly supervised methods are proposed where only image-level labels are required. The two methods share some similar motivations. First of all, since pixel-level masks are missing in this particular setting, the two methods are all designed to estimate the missing ground-truth and further use them as pseudo ground-truth for training. Secondly, they both use data retrieved from the internet as auxiliary data because web data are cheap to obtain and exist in a large amount. Although there are similarities between these two methods, they are designed from different perspectives. The motivation for the first method is that given a group of images crawled from the internet that belong to the same semantic category, it is a good choice to use co-segmentation to extract the masks of them, which gives us almost free pixel-wise training samples. Those internet images along with the extracted masks are used to train a mask generator to help us estimate the pseudo ground-truth for the training images. The second method is designed as a bi-directional framework between the target domain and the web domain. The term “bi-directional” refers to the concept that the… Advisors/Committee Members: Shen, Chunhua (advisor), School of Computer Science (school).

Subjects/Keywords: weakly supervised learning; semantic segmentation

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APA (6^th Edition):

Shen, T. (2018). Context Learning and Weakly Supervised Learning for Semantic Segmentation. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/120354

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Shen, Tong. “Context Learning and Weakly Supervised Learning for Semantic Segmentation.” 2018. Thesis, University of Adelaide. Accessed January 17, 2021. http://hdl.handle.net/2440/120354.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Shen, Tong. “Context Learning and Weakly Supervised Learning for Semantic Segmentation.” 2018. Web. 17 Jan 2021.

Vancouver:

Shen T. Context Learning and Weakly Supervised Learning for Semantic Segmentation. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/2440/120354.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shen T. Context Learning and Weakly Supervised Learning for Semantic Segmentation. [Thesis]. University of Adelaide; 2018. Available from: http://hdl.handle.net/2440/120354

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

7. Van Hecke, K.G. (author). Persistent self-supervised learning principle: Study and demonstration on flying robots.

Degree: 2015, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd

►

We introduce, study and demonstrate Persistent Self-Supervised Learning (PSSL), a machine learning method for usage onboard robotic platforms. The PSSL model leverages a standard supervised… (more)

Subjects/Keywords: persistent self-supervised learning; MAV

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APA (6^th Edition):

Van Hecke, K. G. (. (2015). Persistent self-supervised learning principle: Study and demonstration on flying robots. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd

Chicago Manual of Style (16^th Edition):

Van Hecke, K G (author). “Persistent self-supervised learning principle: Study and demonstration on flying robots.” 2015. Masters Thesis, Delft University of Technology. Accessed January 17, 2021. http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd.

MLA Handbook (7^th Edition):

Van Hecke, K G (author). “Persistent self-supervised learning principle: Study and demonstration on flying robots.” 2015. Web. 17 Jan 2021.

Vancouver:

Van Hecke KG(. Persistent self-supervised learning principle: Study and demonstration on flying robots. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2021 Jan 17]. Available from: http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd.

Council of Science Editors:

Van Hecke KG(. Persistent self-supervised learning principle: Study and demonstration on flying robots. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd

Colorado School of Mines

8. Jackson, Ryan Blake. Machine learning for encrypted Amazon Echo traffic classification.

Degree: MS(M.S.), Computer Science, 2018, Colorado School of Mines

URL: http://hdl.handle.net/11124/172223

► As smart speakers like the Amazon Echo become more popular, they have given rise to rampant concerns regarding user privacy. This work investigates machine learning… (more)

Subjects/Keywords: supervised classification; machine learning

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APA (6^th Edition):

Jackson, R. B. (2018). Machine learning for encrypted Amazon Echo traffic classification. (Masters Thesis). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/172223

Chicago Manual of Style (16^th Edition):

Jackson, Ryan Blake. “Machine learning for encrypted Amazon Echo traffic classification.” 2018. Masters Thesis, Colorado School of Mines. Accessed January 17, 2021. http://hdl.handle.net/11124/172223.

MLA Handbook (7^th Edition):

Jackson, Ryan Blake. “Machine learning for encrypted Amazon Echo traffic classification.” 2018. Web. 17 Jan 2021.

Vancouver:

Jackson RB. Machine learning for encrypted Amazon Echo traffic classification. [Internet] [Masters thesis]. Colorado School of Mines; 2018. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/11124/172223.

Council of Science Editors:

Jackson RB. Machine learning for encrypted Amazon Echo traffic classification. [Masters Thesis]. Colorado School of Mines; 2018. Available from: http://hdl.handle.net/11124/172223

Oklahoma State University

9. Spain, Marc. Study on log event noise reduction by using Naive Bayes supervised machine learning.

Degree: Computer Science, 2019, Oklahoma State University

URL: http://hdl.handle.net/11244/324912

► This research addresses which Naive Bayes model would be best to predict Windows log events that could be considered noise or in other words not… (more)

Subjects/Keywords: naive bayes; supervised machine learning

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APA (6^th Edition):

Spain, M. (2019). Study on log event noise reduction by using Naive Bayes supervised machine learning. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/324912

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Spain, Marc. “Study on log event noise reduction by using Naive Bayes supervised machine learning.” 2019. Thesis, Oklahoma State University. Accessed January 17, 2021. http://hdl.handle.net/11244/324912.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Spain, Marc. “Study on log event noise reduction by using Naive Bayes supervised machine learning.” 2019. Web. 17 Jan 2021.

Vancouver:

Spain M. Study on log event noise reduction by using Naive Bayes supervised machine learning. [Internet] [Thesis]. Oklahoma State University; 2019. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/11244/324912.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Spain M. Study on log event noise reduction by using Naive Bayes supervised machine learning. [Thesis]. Oklahoma State University; 2019. Available from: http://hdl.handle.net/11244/324912

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Wayne State University

10. Hailat, Zeyad. Deep Learning Methods For Visual Object Recognition.

Degree: PhD, Computer Science, 2018, Wayne State University

URL: https://digitalcommons.wayne.edu/oa_dissertations/2026

► Convolutional neural networks (CNNs) attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, curating sufficiently large… (more)

▼ Convolutional neural networks (CNNs) attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, curating sufficiently large labeled training dataset requires human involvement, which is expensive, time-consuming, and susceptible to noisy labels. Semi-supervised learning methods can alleviate the aforementioned problems by employing one of two techniques. First, utilizing a limited number of labeled data in conjunction with sufficiently large unlabeled data to construct a classification model. Second, exploiting sufficiently large noisy label training data to learn a classification model. In this dissertation, we proposed a few new methods to mitigate the aforementioned problems. We summarize our main contributions in three main facets describe below. First, we presented anew Hybrid Residual Network Method (HyResNet) that exploits the power of both supervised and unsupervised deep learning methods into a single deep supervised learning model. Our experiments show the efficacy of HyResNet on visual object recognition tasks. We tested HyResNet on benchmark datasets with various configurations and settings. HyResNet showed comparable results to the state-of-the-art methods on the benchmark datasets. Second, we proposed a deep semi-supervised learning method (DSSL). DSSL utilizes both supervised and unsupervised neural networks. The novelty of DSSL originates from its nature in employing a limited number of labeled training examples in conjunction with sufficiently large unlabeled examples to create a classification model. The combination of DSSL architecture and self-training has a joint impact on the performance over the DSSL. We measured the performance of DSSL method on five benchmark datasets with various labeled / unlabeled levels of training examples and then compared our results with state-of-the-art methods. The experiments show that DSSL sets a new state-of-the-art record for various benchmark tasks. Finally, we introduced a new teacher/student semi-supervised deep learning methods (TS-DSSL). TS-DSSL accepts an input of noisy label training dataset then it employs a self-training and self-cleansing techniques to train a deep learning model. The integration of TS-DSSL architecture with the training protocol maintain the stability of the model and enhance the overall model performance. We evaluated the performance of TS-DSSL on benchmark semi-supervised learning tasks with different levels of noisy labels synthesized from different noise distributions. The experiments showed that TS-DSSL sets a new state-of-the-art record on the benchmark tasks. Advisors/Committee Members: XUEWEN CHEN.

Subjects/Keywords: artificial intelligence; deep learning; machine learning; self-learning; semi-supervised learning; supervised learning; Computer Sciences

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APA (6^th Edition):

Hailat, Z. (2018). Deep Learning Methods For Visual Object Recognition. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/2026

Chicago Manual of Style (16^th Edition):

Hailat, Zeyad. “Deep Learning Methods For Visual Object Recognition.” 2018. Doctoral Dissertation, Wayne State University. Accessed January 17, 2021. https://digitalcommons.wayne.edu/oa_dissertations/2026.

MLA Handbook (7^th Edition):

Hailat, Zeyad. “Deep Learning Methods For Visual Object Recognition.” 2018. Web. 17 Jan 2021.

Vancouver:

Hailat Z. Deep Learning Methods For Visual Object Recognition. [Internet] [Doctoral dissertation]. Wayne State University; 2018. [cited 2021 Jan 17]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/2026.

Council of Science Editors:

Hailat Z. Deep Learning Methods For Visual Object Recognition. [Doctoral Dissertation]. Wayne State University; 2018. Available from: https://digitalcommons.wayne.edu/oa_dissertations/2026

University of Sydney

11. He, Fengxiang. Instance-Dependent Positive-Unlabelled Learning .

Degree: 2018, University of Sydney

URL: http://hdl.handle.net/2123/20115

► An emerging topic in machine learning is how to learn classifiers from datasets containing only positive and unlabelled examples (PU learning). This problem has significant… (more)

▼ An emerging topic in machine learning is how to learn classifiers from datasets containing only positive and unlabelled examples (PU learning). This problem has significant importance in both academia and industry. This thesis addresses the PU learning problem following a natural strategy that treats unlabelled data as negative. By this way, a PU dataset is transferred to a fully-labelled dataset but with label noise. This strategy has been employed by many existing works and is usually called the one-side noise model. Under the framework of the one-side noise model, this thesis proposes an instance-dependent model to express how likely a negative label is corrupted. The model relies on the probabilistic gap, which is defined as the difference between the posteriors that an instance is respectively from the classes of positive or negative. Intuitively, the instance with a smaller probabilistic gap is more likely to be wrongly labelled. Motivated by this intuition, this thesis assumes there is a negative correlation between the noisy probability of the instance and the corresponding probabilistic gap. This model is named as probabilistic-gap PU model (PGPU model). Based on the PGPU model, this thesis designs Bayesian relabelling method that can select a group of the unlabelled instances and give them new labels that are identical to the ones assigned by a Bayesian optimal classifier. By this way, we can significantly extend the labelled dataset. Eventually, this thesis employs conventional binary classification methods to learn a classifier from the extended labelled datasets. It is worth noting that there could be a sub-domain of the instances where no data point can be relabelled. This issue could lead to a biased classifier. A kernel mean matching technique is then employed to remedy this problem. This thesis also evaluates the proposed method in both theoretical and empirical manners. Both theoretical and empirical results are in agreements with our method.

Subjects/Keywords: Postive-unlabelled learning; weakly supervised learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

He, F. (2018). Instance-Dependent Positive-Unlabelled Learning . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/20115

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

He, Fengxiang. “Instance-Dependent Positive-Unlabelled Learning .” 2018. Thesis, University of Sydney. Accessed January 17, 2021. http://hdl.handle.net/2123/20115.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

He, Fengxiang. “Instance-Dependent Positive-Unlabelled Learning .” 2018. Web. 17 Jan 2021.

Vancouver:

He F. Instance-Dependent Positive-Unlabelled Learning . [Internet] [Thesis]. University of Sydney; 2018. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/2123/20115.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

He F. Instance-Dependent Positive-Unlabelled Learning . [Thesis]. University of Sydney; 2018. Available from: http://hdl.handle.net/2123/20115

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Texas – Austin

12. Joshi, Shalmali Dilip. Constraint based approaches to interpretable and semi-supervised machine learning.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Texas – Austin

URL: http://dx.doi.org/10.26153/tsw/1259

► Interpretability and Explainability of machine learning algorithms are becoming increasingly important as Machine Learning (ML) systems get widely applied to domains like clinical healthcare, social… (more)

▼ Interpretability and Explainability of machine learning algorithms are becoming increasingly important as Machine Learning (ML) systems get widely applied to domains like clinical healthcare, social media and governance. A related major challenge in deploying ML systems pertains to reliable learning when expert annotation is severely limited. This dissertation prescribes a common framework to address these challenges, based on the use of constraints that can make an ML model more interpretable, lead to novel methods for explaining ML models, or help to learn reliably with limited supervision. In particular, we focus on the class of latent variable models and develop a general learning framework by constraining realizations of latent variables and/or model parameters. We propose specific constraints that can be used to develop identifiable latent variable models, that in turn learn interpretable outcomes. The proposed framework is first used in Non–negative Matrix Factorization and Probabilistic Graphical Models. For both models, algorithms are proposed to incorporate such constraints with seamless and tractable augmentation of the associated learning and inference procedures. The utility of the proposed methods is demonstrated for our working application domain – identifiable phenotyping using Electronic Health Records (EHRs). Evaluation by domain experts reveals that the proposed models are indeed more clinically relevant (and hence more interpretable) than existing counterparts. The work also demonstrates that while there may be inherent trade–offs between constraining models to encourage interpretability, the quantitative performance of downstream tasks remains competitive. We then focus on constraint based mechanisms to explain decisions or outcomes of supervised black-box models. We propose an explanation model based on generating examples where the nature of the examples is constrained i.e. they have to be sampled from the underlying data domain. To do so, we train a generative model to characterize the data manifold in a high dimensional ambient space. Constrained sampling then allows us to generate naturalistic examples that lie along the data manifold. We propose ways to summarize model behavior using such constrained examples. In the last part of the contributions, we argue that heterogeneity of data sources is useful in situations where very little to no supervision is available. This thesis leverages such heterogeneity (via constraints) for two critical but widely different machine learning algorithms. In each case, a novel algorithm in the sub-class of co–regularization is developed to combine information from heterogeneous sources. Co–regularization is a framework of constraining latent variables and/or latent distributions in order to leverage heterogeneity. The proposed algorithms are utilized for clustering, where the intent is to generate a partition or grouping of observed samples, and for Learning to Rank algorithms – used to rank a set of observed samples in order of preference with respect… Advisors/Committee Members: Ghosh, Joydeep (advisor), Koyejo, Oluwasanmi (committee member), Sanghavi, Sujay (committee member), Vikalo, Haris (committee member), Mooney, Raymond (committee member).

Subjects/Keywords: Interpretable machine learning; Semi-supervised machine learning

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APA (6^th Edition):

Joshi, S. D. (2019). Constraint based approaches to interpretable and semi-supervised machine learning. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/1259

Chicago Manual of Style (16^th Edition):

Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed January 17, 2021. http://dx.doi.org/10.26153/tsw/1259.

MLA Handbook (7^th Edition):

Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Web. 17 Jan 2021.

Vancouver:

Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2021 Jan 17]. Available from: http://dx.doi.org/10.26153/tsw/1259.

Council of Science Editors:

Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/1259

13. Byun, Byungki. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.

Degree: PhD, Electrical and Computer Engineering, 2012, Georgia Tech

URL: http://hdl.handle.net/1853/43597

► This dissertation presents the development of a semi-supervised incremental learning framework with a multi-view perspective for image concept modeling. For reliable image concept characterization, having… (more)

Subjects/Keywords: Discriminative learning; Semi-supervised learning; Incremental learning; Image modeling; Multi-view learning; Machine learning; Supervised learning (Machine learning); Boosting (Algorithms)

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APA (6^th Edition):

Byun, B. (2012). On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/43597

Chicago Manual of Style (16^th Edition):

Byun, Byungki. “On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.” 2012. Doctoral Dissertation, Georgia Tech. Accessed January 17, 2021. http://hdl.handle.net/1853/43597.

MLA Handbook (7^th Edition):

Byun, Byungki. “On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.” 2012. Web. 17 Jan 2021.

Vancouver:

Byun B. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/1853/43597.

Council of Science Editors:

Byun B. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/43597

University of Alberta

14. Mahmood, Ashique. Automatic step-size adaptation in incremental supervised learning.

Degree: MS, Department of Computing Science, 2010, University of Alberta

URL: https://era.library.ualberta.ca/files/zc77sr03r

► Performance and stability of many iterative algorithms such as stochastic gradient descent largely depend on a fixed and scalar step-size parameter. Use of a fixed… (more)

Subjects/Keywords: step size; supervised learning; stochastic gradient descent

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APA (6^th Edition):

Mahmood, A. (2010). Automatic step-size adaptation in incremental supervised learning. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/zc77sr03r

Chicago Manual of Style (16^th Edition):

Mahmood, Ashique. “Automatic step-size adaptation in incremental supervised learning.” 2010. Masters Thesis, University of Alberta. Accessed January 17, 2021. https://era.library.ualberta.ca/files/zc77sr03r.

MLA Handbook (7^th Edition):

Mahmood, Ashique. “Automatic step-size adaptation in incremental supervised learning.” 2010. Web. 17 Jan 2021.

Vancouver:

Mahmood A. Automatic step-size adaptation in incremental supervised learning. [Internet] [Masters thesis]. University of Alberta; 2010. [cited 2021 Jan 17]. Available from: https://era.library.ualberta.ca/files/zc77sr03r.

Council of Science Editors:

Mahmood A. Automatic step-size adaptation in incremental supervised learning. [Masters Thesis]. University of Alberta; 2010. Available from: https://era.library.ualberta.ca/files/zc77sr03r

Georgia Tech

15. Ahsan, Unaiza. Leveraging mid-level representations for complex activity recognition.

Degree: PhD, Interactive Computing, 2019, Georgia Tech

URL: http://hdl.handle.net/1853/61199

► Dynamic scene understanding requires learning representations of the components of the scene including objects, environments, actions and events. Complex activity recognition from images and videos… (more)

▼ Dynamic scene understanding requires learning representations of the components of the scene including objects, environments, actions and events. Complex activity recognition from images and videos requires annotating large datasets with action labels which is a tedious and expensive task. Thus, there is a need to design a mid-level or intermediate feature representation which does not require millions of labels, yet is able to generalize to semantic-level recognition of activities in visual data. This thesis makes three contributions in this regard. First, we propose an event concept-based intermediate representation which learns concepts via the Web and uses this representation to identify events even with a single labeled example. To demonstrate the strength of the proposed approaches, we contribute two diverse social event datasets to the community. We then present a use case of event concepts as a mid-level representation that generalizes to sentiment recognition in diverse social event images. Second, we propose to train Generative Adversarial Networks (GANs) with video frames (which does not require labels), use the trained discriminator from GANs as an intermediate representation and finetune it on a smaller labeled video activity dataset to recognize actions in videos. This unsupervised pre-training step avoids any manual feature engineering, video frame encoding or searching for the best video frame sampling technique. Our third contribution is a self-supervised learning approach on videos that exploits both spatial and temporal coherency to learn feature representations on video data without any supervision. We demonstrate the transfer learning capability of this model on smaller labeled datasets. We present comprehensive experimental analysis on the self-supervised model to provide insights into the unsupervised pretraining paradigm and how it can help with activity recognition on target datasets which the model has never seen during training. Advisors/Committee Members: Essa, Irfan (advisor), Hays, James (committee member), De Choudhury, Munmun (committee member), Kira, Zsolt (committee member), Parikh, Devi (committee member), Sun, Chen (committee member).

Subjects/Keywords: Activity recognition; Self-supervised learning; Event recognition

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APA (6^th Edition):

Ahsan, U. (2019). Leveraging mid-level representations for complex activity recognition. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61199

Chicago Manual of Style (16^th Edition):

Ahsan, Unaiza. “Leveraging mid-level representations for complex activity recognition.” 2019. Doctoral Dissertation, Georgia Tech. Accessed January 17, 2021. http://hdl.handle.net/1853/61199.

MLA Handbook (7^th Edition):

Ahsan, Unaiza. “Leveraging mid-level representations for complex activity recognition.” 2019. Web. 17 Jan 2021.

Vancouver:

Ahsan U. Leveraging mid-level representations for complex activity recognition. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/1853/61199.

Council of Science Editors:

Ahsan U. Leveraging mid-level representations for complex activity recognition. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61199

McMaster University

16. Ateeq, Sameen. Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.

Degree: MSc, 2018, McMaster University

URL: http://hdl.handle.net/11375/24095

►

According to the Public Health Agency of Canada, falls account for 95% of all hip fractures in Canada; 20% of fall-related injury cases end in… (more)

Subjects/Keywords: machine learning; supervised classification; falls; CCHS; injuries

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APA (6^th Edition):

Ateeq, S. (2018). Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/24095

Chicago Manual of Style (16^th Edition):

Ateeq, Sameen. “Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.” 2018. Masters Thesis, McMaster University. Accessed January 17, 2021. http://hdl.handle.net/11375/24095.

MLA Handbook (7^th Edition):

Ateeq, Sameen. “Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.” 2018. Web. 17 Jan 2021.

Vancouver:

Ateeq S. Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/11375/24095.

Council of Science Editors:

Ateeq S. Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. [Masters Thesis]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/24095

Baylor University

17. -1697-5430. Semi-supervised learning for electrocardiography signal classification.

Degree: M.S.E.C.E., Baylor University. Dept. of Electrical & Computer Engineering., 2018, Baylor University

URL: http://hdl.handle.net/2104/10391

► An electrocardiogram (ECG) is a cardiology test that provides information about the structure and function of the heart. The size of the ECG data collected… (more)

Subjects/Keywords: Semi-supervised learning; Electrocardiography; pattern recognition

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APA (6^th Edition):

-1697-5430. (2018). Semi-supervised learning for electrocardiography signal classification. (Masters Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/10391

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

-1697-5430. “Semi-supervised learning for electrocardiography signal classification.” 2018. Masters Thesis, Baylor University. Accessed January 17, 2021. http://hdl.handle.net/2104/10391.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

-1697-5430. “Semi-supervised learning for electrocardiography signal classification.” 2018. Web. 17 Jan 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-1697-5430. Semi-supervised learning for electrocardiography signal classification. [Internet] [Masters thesis]. Baylor University; 2018. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/2104/10391.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-1697-5430. Semi-supervised learning for electrocardiography signal classification. [Masters Thesis]. Baylor University; 2018. Available from: http://hdl.handle.net/2104/10391

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

University of Manchester

18. Rostamniakankalhori, Sharareh. Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course.

Degree: 2011, University of Manchester

URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404

► Tuberculosis (TB) is an infectious disease which is a global public health problem with over 9 million new cases annually. Tuberculosis treatment, with patient supervision… (more)

▼ Tuberculosis (TB) is an infectious disease which is a global public health problem with over 9 million new cases annually. Tuberculosis treatment, with patient supervision and support is an element of the global plan to stop TB designed by the World Health Organization in 2006. The plan requires prediction of patient treatment course destination. The prediction outcome can be used to determine how intensive the level of supplying services and supports in frame of DOTS therapy should be. No predictive model for the outcome has been developed yet and only limited reports of influential factors for considered outcome are available.To fill this gap, this thesis develops a machine learning approach to predict the outcome of tuberculosis treatment course, which includes, firstly, data of 6,450 Iranian TB patients under DOTS (directly observed treatment, short course ) therapy were analysed to initially diagnose the significant predictors by correlation analysis; secondly, these significant features were applied to find the best classification approach from six examined algorithms including decision tree, Bayesian network, logistic regression, multilayer perceptron, radial basis function, and support vector machine; thirdly, the prediction accuracy of these existing techniques was improved by proposing and developing a new integrated method of k-mean clustering and classification algorithms. Finally, a cluster-based simplified decision tree (CSDT) was developed through an innovative hierarchical clustering and classification algorithm. CSDT was built by k-mean partitioning and the decision tree learning. This innovative method not only improves the prediction accuracy significantly but also leads to a much simpler and interpretative decision tree.The main results of this study included, firstly, finding seventeen significantly correlated features which were: age, sex, weight, nationality, area of residency, current stay in prison, low body weight, TB type, treatment category, length of disease, TB case type, recent TB infection, diabetic or HIV positive, and social risk factors like history of imprisonment, IV drug usage, and unprotected sex ; secondly, the results by applying and comparing six applied supervised machine learning tools on the testing set revealed that decision trees gave the best prediction accuracy (74.21%) compared with other methods; thirdly, by using testing set, the new integrated approach to combine the clustering and classification approach leads to the prediction accuracy improvement for all applied classifiers; the most and least improvement for prediction accuracy were shown by logistic regression (10%) and support vector machine (4%) respectively. Finally, by applying the proposed and developed CSDT, cluster-based simplified decision trees were optioned, which reduced the size of the resulting decision tree and further improved the prediction accuracy.Data type and having normal distribution have created an opportunity for the decision tree to outperform other algorithms. Pre-learning by k-mean… Advisors/Committee Members: Zeng, Xiaojun.

Subjects/Keywords: Integrated Supervised and Unsupervised Learning; Tuberculosis; plediction

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APA (6^th Edition):

Rostamniakankalhori, S. (2011). Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404

Chicago Manual of Style (16^th Edition):

Rostamniakankalhori, Sharareh. “Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course.” 2011. Doctoral Dissertation, University of Manchester. Accessed January 17, 2021. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404.

MLA Handbook (7^th Edition):

Rostamniakankalhori, Sharareh. “Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course.” 2011. Web. 17 Jan 2021.

Vancouver:

Rostamniakankalhori S. Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course. [Internet] [Doctoral dissertation]. University of Manchester; 2011. [cited 2021 Jan 17]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404.

Council of Science Editors:

Rostamniakankalhori S. Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course. [Doctoral Dissertation]. University of Manchester; 2011. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404

19. Zhao, Xuran. Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.

Degree: Docteur es, Signal et images, 2013, Paris, ENST

URL: http://www.theses.fr/2013ENST0061

►

Dans la plupart des systèmes biométriques de l’état de l’art, les données biométrique sont souvent représentés par des vecteurs de grande dimensionalité. La dimensionnalité d'éléments… (more)

▼

Dans la plupart des systèmes biométriques de l’état de l’art, les données biométrique sont souvent représentés par des vecteurs de grande dimensionalité. La dimensionnalité d'éléments biométriques génèrent un problème de malédiction de dimensionnalité. Dans la biométrie multimodale, différentes modalités biométriques peuvent former différents entrés des algorithmes de classification. La fusion des modalités reste un problème difficile et est généralement traitée de manière isolée à celui de dimensionalité élevée. Cette thèse aborde le problème de la dimensionnalité élevée et le problème de la fusion multimodale dans un cadre unifié. En vertu d'un paramètre biométrique multi-modale et les données non étiquetées abondantes données, nous cherchons à extraire des caractéristiques discriminatoires de multiples modalités d'une manière non supervisée. Les contributions de cette thèse sont les suivantes: Un état de l’art des algorithmes RMVD de l'état de l'art ; Un nouveau concept de RMVD: accord de la structure de données dans sous-espace; Trois nouveaux algorithmes de MVDR basée sur des définitions différentes de l’accord de la structure dans les sous-espace; L’application des algorithmes proposés à la classification semi-supervisée, la classification non supervisée, et les problèmes de récupération de données biométriques, en particulier dans un contexte de la reconnaissance de personne en audio et vidéo; L’application des algorithmes proposés à des problèmes plus larges de reconnaissance des formes pour les données non biométriques, tels que l'image et le regroupement de texte et la recherche.

Biometric data is often represented by high-dimensional feature vectors which contain significant inter-session variation. Discriminative dimensionality reduction techniques generally follow a supervised learning scheme. However, labelled training data is generally limited in quantity and often does not reliably represent the inter-session variation encountered in test data. This thesis proposes to use multi-view dimensionality reduction (MVDR) which aims to extract discriminative features in multi-modal biometric systems, where different modalities are regarded as different views of the same data. MVDR projections are trained on feature-feature pairs where label information is not required. Since unlabelled data is easier to acquire in large quantities, and because of the natural co-existence of multiple views in multi-modal biometric problems, discriminant, low-dimensional subspaces can be learnt using the proposed MVDR approaches in a largely unsupervised manner. According to different functionalities of biometric systems, namely, clustering, and retrieval, we propose three MVDR frameworks which meet the requirements for each functionality. The proposed approaches, however, share the same spirit: all methods aim to learn a projection for each view such that a certain form of agreement is attained in the subspaces across different views. The proposed MVDR frameworks can thus be unified into one general framework for multi-view…

Advisors/Committee Members: Evans, Nicholas W. D. (thesis director), Dugelay, Jean-Luc (thesis director).

Subjects/Keywords: Apprentissage semi-supervisé; Semi-supervised learning

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APA (6^th Edition):

Zhao, X. (2013). Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. (Doctoral Dissertation). Paris, ENST. Retrieved from http://www.theses.fr/2013ENST0061

Chicago Manual of Style (16^th Edition):

Zhao, Xuran. “Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.” 2013. Doctoral Dissertation, Paris, ENST. Accessed January 17, 2021. http://www.theses.fr/2013ENST0061.

MLA Handbook (7^th Edition):

Zhao, Xuran. “Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.” 2013. Web. 17 Jan 2021.

Vancouver:

Zhao X. Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. [Internet] [Doctoral dissertation]. Paris, ENST; 2013. [cited 2021 Jan 17]. Available from: http://www.theses.fr/2013ENST0061.

Council of Science Editors:

Zhao X. Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. [Doctoral Dissertation]. Paris, ENST; 2013. Available from: http://www.theses.fr/2013ENST0061

Delft University of Technology

20. Paramkusam, Deepak (author). Comparison of Optimal Control Techniques for Learning-based RRT.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:742ed24e-0525-4ae2-b6d4-2dc6f69e60e1

► Kinodynamic motion planning for a robot involves generating a trajectory from a given robot state to goal state while satisfying kinematic and dynamic constraints. Rapidly-exploring… (more)

▼ Kinodynamic motion planning for a robot involves generating a trajectory from a given robot state to goal state while satisfying kinematic and dynamic constraints. Rapidly-exploring Random Trees (RRT) is a sampling-based algorithm that has been widely adopted for this. However, RRT is not fast enough to enable its use in industrial applications. Recently, supervised learning has been used to pre-learn time consuming steps of RRT which resulted in improvement in planning times. The supervised learning models require cost and control input of the system as training data which are generated using optimal control. The training data can be obtained either by indirect optimal control or direct optimal control techniques. In this thesis, both the techniques are each used to generate cost and control inputs for a two-link manipulator using random initial-final state pairs. Then each dataset is used to train a model and the datasets are compared based on certain training metrics. K-nearest neighbours regression and multi-layer perceptron neural network are the supervised learning models used in this thesis. It is observed that both the datasets result in similar convergence of the models, but indirect optimal control approach allows upto 24-fold faster data generation and upto 3-fold reduction in dimensionality of training data compared to the direct optimal approach. Real-world robots have torque limits based on actuator configuration. The torque limits are modeled as control constraints in both the optimal control techniques and the effect of this restriction on data generation and supervised learning is studied in this thesis. Direct optimal control is found to be better for data generation in this case due to the ease of applying control bounds as inequality constraints on the function approximations. Indirect optimal control is very tedious as active constraints should be known a priori to determine the switching points. An alternate method is explored instead where samples are generated similar to the unconstrained case but samples violating the constraints are removed. Poor control input learning is observed in both approaches and the models struggled to extrapolate. It is hypothesised that this is due to inability of the constrained data to fully capture the system dynamics. However, good cost prediction is achieved using neural networks. Advisors/Committee Members: Wisse, Martijn (mentor), Bharatheesha, Mukunda (mentor), Grammatico, Sergio (graduation committee), Wolfslag, Wouter (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: RRT; Supervised Learning; Optimal control; Motion Planning

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APA (6^th Edition):

Paramkusam, D. (. (2018). Comparison of Optimal Control Techniques for Learning-based RRT. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:742ed24e-0525-4ae2-b6d4-2dc6f69e60e1

Chicago Manual of Style (16^th Edition):

Paramkusam, Deepak (author). “Comparison of Optimal Control Techniques for Learning-based RRT.” 2018. Masters Thesis, Delft University of Technology. Accessed January 17, 2021. http://resolver.tudelft.nl/uuid:742ed24e-0525-4ae2-b6d4-2dc6f69e60e1.

MLA Handbook (7^th Edition):

Paramkusam, Deepak (author). “Comparison of Optimal Control Techniques for Learning-based RRT.” 2018. Web. 17 Jan 2021.

Vancouver:

Paramkusam D(. Comparison of Optimal Control Techniques for Learning-based RRT. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 17]. Available from: http://resolver.tudelft.nl/uuid:742ed24e-0525-4ae2-b6d4-2dc6f69e60e1.

Council of Science Editors:

Paramkusam D(. Comparison of Optimal Control Techniques for Learning-based RRT. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:742ed24e-0525-4ae2-b6d4-2dc6f69e60e1

Delft University of Technology

21. Moring, Stefan (author). Kinodynamic Steering using Supervised Learning in RRT.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:fdf13674-f2b5-4b4a-b03d-21299114642a

►

With the need for robots to operate autonomously increasing more and more, the research field of motion planning is becoming more active. Usually planning is… (more)

Subjects/Keywords: Motion Planning; RRT; Kinodynamic; Supervised Learning

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APA (6^th Edition):

Moring, S. (. (2018). Kinodynamic Steering using Supervised Learning in RRT. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:fdf13674-f2b5-4b4a-b03d-21299114642a

Chicago Manual of Style (16^th Edition):

Moring, Stefan (author). “Kinodynamic Steering using Supervised Learning in RRT.” 2018. Masters Thesis, Delft University of Technology. Accessed January 17, 2021. http://resolver.tudelft.nl/uuid:fdf13674-f2b5-4b4a-b03d-21299114642a.

MLA Handbook (7^th Edition):

Moring, Stefan (author). “Kinodynamic Steering using Supervised Learning in RRT.” 2018. Web. 17 Jan 2021.

Vancouver:

Moring S(. Kinodynamic Steering using Supervised Learning in RRT. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 17]. Available from: http://resolver.tudelft.nl/uuid:fdf13674-f2b5-4b4a-b03d-21299114642a.

Council of Science Editors:

Moring S(. Kinodynamic Steering using Supervised Learning in RRT. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:fdf13674-f2b5-4b4a-b03d-21299114642a

University of Victoria

22. Sharma, Mridula. Evaluating and enhancing the security of cyber physical systems using machine learning approaches.

Degree: Department of Electrical and Computer Engineering, 2020, University of Victoria

URL: http://hdl.handle.net/1828/11675

► The main aim of this dissertation is to address the security issues of the physical layer of Cyber Physical Systems. The network security is first… (more)

Subjects/Keywords: CPS; Supervised Machine Learning; RPL; Feature Selection

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APA (6^th Edition):

Sharma, M. (2020). Evaluating and enhancing the security of cyber physical systems using machine learning approaches. (Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/11675

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sharma, Mridula. “Evaluating and enhancing the security of cyber physical systems using machine learning approaches.” 2020. Thesis, University of Victoria. Accessed January 17, 2021. http://hdl.handle.net/1828/11675.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sharma, Mridula. “Evaluating and enhancing the security of cyber physical systems using machine learning approaches.” 2020. Web. 17 Jan 2021.

Vancouver:

Sharma M. Evaluating and enhancing the security of cyber physical systems using machine learning approaches. [Internet] [Thesis]. University of Victoria; 2020. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/1828/11675.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sharma M. Evaluating and enhancing the security of cyber physical systems using machine learning approaches. [Thesis]. University of Victoria; 2020. Available from: http://hdl.handle.net/1828/11675

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Notre Dame

23. Troy William Raeder. Evaluating and Maintaining Classification Algorithms</h1>.

Degree: Computer Science and Engineering, 2012, University of Notre Dame

URL: https://curate.nd.edu/show/4b29b56616h

► Any practical application of machine learning necessarily begins with the selection of a classification algorithm. Generally, practitioners will try several different types of algorithms… (more)

Subjects/Keywords: classification; supervised learning; evaluation; concept drift

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APA (6^th Edition):

Raeder, T. W. (2012). Evaluating and Maintaining Classification Algorithms</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/4b29b56616h

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Raeder, Troy William. “Evaluating and Maintaining Classification Algorithms</h1>.” 2012. Thesis, University of Notre Dame. Accessed January 17, 2021. https://curate.nd.edu/show/4b29b56616h.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Raeder, Troy William. “Evaluating and Maintaining Classification Algorithms</h1>.” 2012. Web. 17 Jan 2021.

Vancouver:

Raeder TW. Evaluating and Maintaining Classification Algorithms</h1>. [Internet] [Thesis]. University of Notre Dame; 2012. [cited 2021 Jan 17]. Available from: https://curate.nd.edu/show/4b29b56616h.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Raeder TW. Evaluating and Maintaining Classification Algorithms</h1>. [Thesis]. University of Notre Dame; 2012. Available from: https://curate.nd.edu/show/4b29b56616h

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Tennessee – Knoxville

24. Amreen, Sadika. Methods of Disambiguating and De-anonymizing Authorship in Large Scale Operational Data.

Degree: 2019, University of Tennessee – Knoxville

URL: https://trace.tennessee.edu/utk_graddiss/5453

► Operational data from software development, social networks and other domains are often contaminated with incorrect or missing values. Examples include misspelled or changed names, multiple… (more)

▼ Operational data from software development, social networks and other domains are often contaminated with incorrect or missing values. Examples include misspelled or changed names, multiple emails belonging to the same person and user profiles that vary in different systems. Such digital traces are extensively used in research and practice to study collaborating communities of various kinds. To achieve a realistic representation of the networks that represent these communities, accurate identities are essential. In this work, we aim to identify, model, and correct identity errors in data from open-source software repositories, which include more than 23M developer IDs and nearly 1B Git commits (developer activity records). Our investigation into the nature and prevalence of identity errors in software activity data reveals that they are different and occur at much higher rates than other domains. Existing techniques relying on string comparisons can only disambiguate Synonyms, but not Homonyms, which are common in software activity traces. Therefore, we introduce measures of behavioral fingerprinting to improve the accuracy of Synonym resolution, and to disambiguate Homonyms. Fingerprints are constructed from the traces of developers’ activities, such as, the style of writing in commit messages, the patterns in files modified and projects participated in by developers, and the patterns related to the timing of the developers’ activity. Furthermore, to address the lack of training data necessary for the supervised learning approaches that are used in disambiguation, we design a specific active learning procedure that minimizes the manual effort necessary to create training data in the domain of developer identity matching. We extensively evaluate the proposed approach, using over 16,000 OpenStack developers in 1200 projects, against commercial and most recent research approaches, and further on recent research on a much larger sample of over 2,000,000 IDs. Results demonstrate that our method is significantly better than both the recent research and commercial methods. We also conduct experiments to demonstrate that such erroneous data have significant impact on developer networks. We hope that the proposed approach will expedite research progress in the domain of software engineering, especially in applications for which graphs of social networks are critical.

Subjects/Keywords: identity disambiguation; supervised learning; behavioral fingerprinting

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Amreen, S. (2019). Methods of Disambiguating and De-anonymizing Authorship in Large Scale Operational Data. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/5453

Chicago Manual of Style (16^th Edition):

Amreen, Sadika. “Methods of Disambiguating and De-anonymizing Authorship in Large Scale Operational Data.” 2019. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed January 17, 2021. https://trace.tennessee.edu/utk_graddiss/5453.

MLA Handbook (7^th Edition):

Amreen, Sadika. “Methods of Disambiguating and De-anonymizing Authorship in Large Scale Operational Data.” 2019. Web. 17 Jan 2021.

Vancouver:

Amreen S. Methods of Disambiguating and De-anonymizing Authorship in Large Scale Operational Data. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2019. [cited 2021 Jan 17]. Available from: https://trace.tennessee.edu/utk_graddiss/5453.

Council of Science Editors:

Amreen S. Methods of Disambiguating and De-anonymizing Authorship in Large Scale Operational Data. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2019. Available from: https://trace.tennessee.edu/utk_graddiss/5453

University of North Texas

25. Dandala, Bharath. Multilingual Word Sense Disambiguation Using Wikipedia.

Degree: 2013, University of North Texas

URL: https://digital.library.unt.edu/ark:/67531/metadc500036/

► Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in… (more)

▼ Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in any given language carrying more than one meaning. Word sense disambiguation is the task of automatically assigning the most appropriate meaning to a polysemous word within a given context. Generally the problem of resolving ambiguity in literature has revolved around the famous quote “you shall know the meaning of the word by the company it keeps.” In this thesis, we investigate the role of context for resolving ambiguity through three different approaches. Instead of using a predefined monolingual sense inventory such as WordNet, we use a language-independent framework where the word senses and sense-tagged data are derived automatically from Wikipedia. Using Wikipedia as a source of sense-annotations provides the much needed solution for knowledge acquisition bottleneck. In order to evaluate the viability of Wikipedia based sense-annotations, we cast the task of disambiguating polysemous nouns as a monolingual classification task and experimented on lexical samples from four different languages (viz. English, German, Italian and Spanish). The experiments confirm that the Wikipedia based sense annotations are reliable and can be used to construct accurate monolingual sense classifiers. It is a long belief that exploiting multiple languages helps in building accurate word sense disambiguation systems. Subsequently, we developed two approaches that recast the task of disambiguating polysemous nouns as a multilingual classification task. The first approach for multilingual word sense disambiguation attempts to effectively use a machine translation system to leverage two relevant multilingual aspects of the semantics of text. First, the various senses of a target word may be translated into different words, which constitute unique, yet highly salient signal that effectively expand the target word’s feature space. Second, the translated context words themselves embed co-occurrence information that a translation engine gathers from very large parallel corpora. The second approach for multlingual word sense disambiguation attempts to reduce the reliance on the machine translation system during training by using the multilingual knowledge available in Wikipedia through its interlingual links. Finally, the experiments on a lexical sample from four different languages confirm that the multilingual systems perform better than the monolingual system and significantly improve the disambiguation accuracy. Advisors/Committee Members: Mihalcea, Rada, 1974-, Tarau, Paul, Nielsen, Rodney, Bunescu, Răzvan.

Subjects/Keywords: Wikipedia; word sense disambiguation; supervised learning; multilingual

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APA (6^th Edition):

Dandala, B. (2013). Multilingual Word Sense Disambiguation Using Wikipedia. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc500036/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Dandala, Bharath. “Multilingual Word Sense Disambiguation Using Wikipedia.” 2013. Thesis, University of North Texas. Accessed January 17, 2021. https://digital.library.unt.edu/ark:/67531/metadc500036/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Dandala, Bharath. “Multilingual Word Sense Disambiguation Using Wikipedia.” 2013. Web. 17 Jan 2021.

Vancouver:

Dandala B. Multilingual Word Sense Disambiguation Using Wikipedia. [Internet] [Thesis]. University of North Texas; 2013. [cited 2021 Jan 17]. Available from: https://digital.library.unt.edu/ark:/67531/metadc500036/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dandala B. Multilingual Word Sense Disambiguation Using Wikipedia. [Thesis]. University of North Texas; 2013. Available from: https://digital.library.unt.edu/ark:/67531/metadc500036/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

26. Biyani, Prakhar. Analyzing Subjectivity and Sentiment of Online Forums.

Degree: 2014, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/22850

► Online social media has emerged as a popular medium for seeking and providing information, opinions and social support. Online sites such as discussion forums, blogs… (more)

Subjects/Keywords: Subjectivity analysis; sentiment analysis; classification; supervised learning; semi-supervised learning; online forums

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Biyani, P. (2014). Analyzing Subjectivity and Sentiment of Online Forums. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/22850

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Biyani, Prakhar. “Analyzing Subjectivity and Sentiment of Online Forums.” 2014. Thesis, Penn State University. Accessed January 17, 2021. https://submit-etda.libraries.psu.edu/catalog/22850.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Biyani, Prakhar. “Analyzing Subjectivity and Sentiment of Online Forums.” 2014. Web. 17 Jan 2021.

Vancouver:

Biyani P. Analyzing Subjectivity and Sentiment of Online Forums. [Internet] [Thesis]. Penn State University; 2014. [cited 2021 Jan 17]. Available from: https://submit-etda.libraries.psu.edu/catalog/22850.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Biyani P. Analyzing Subjectivity and Sentiment of Online Forums. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/22850

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oregon State University

27. Hao, Guohua. Efficient training and feature induction in sequential supervised learning.

Degree: PhD, Computer Science, 2009, Oregon State University

URL: http://hdl.handle.net/1957/12548

► Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature… (more)

▼ Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential supervised learning problems. Existing training algorithms for CRFs are slow, particularly in problems with large numbers of potential input features and feature combinations. In this dissertation, we describe a new algorithm, TreeCRF, for training CRFs via gradient tree boosting. In TreeCRF, the CRF potential functions are represented as weighted sums of regression trees, which provide compact representations of feature interactions. So the algorithm does not explicitly consider the potentially large parameter space. As a result, gradient tree boosting scales linearly in the order of the Markov model and in the order of the feature interactions, rather than exponentially as in previous algorithms based on iterative scaling and gradient descent. Detailed experimental results are provided to evaluate the performance of the TreeCRF algorithm and possible extensions of this algorithm are discussed. We also study the problem of handling missing input values in CRFs, which has been rarely discussed in the literature. Gradient tree boosting also makes it possible to use instance weighting (as in C4.5) and surrogate splitting (as in CART) to handle missing values in CRFs. Experimental studies of the effectiveness of these two methods (as well as standard imputation and indicator feature methods) show that instance weighting is the best method in most cases when feature values are missing at random. In the direction of feature induction, we study the search-based structured learning framework and its application to sequential supervised learning problems. By formulating the label sequence prediction process as an incremental search process from one end of a sequence to the other, this framework is able to avoid complicated inference algorithms in the training process and thus achieves very fast training speed. However, for problems where there exist long range dependencies between the current position and future positions, at each search step, this framework is unable to exploit these dependencies to make accurate predictions. In this dissertation, a multiple-instance learning based algorithm is proposed to automatically extract useful features from future positions as a way to discover and exploit these long range dependencies. Integrating this algorithm with maximum entropy Markov models yields promising experimental results on both synthetic data sets and real data sets that have long range dependencies in sequences. Advisors/Committee Members: Dietterich, Thomas G. (advisor), Fern, Alan (committee member).

Subjects/Keywords: Machine Learning; Supervised learning (Machine learning) – Mathematical models

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APA (6^th Edition):

Hao, G. (2009). Efficient training and feature induction in sequential supervised learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/12548

Chicago Manual of Style (16^th Edition):

Hao, Guohua. “Efficient training and feature induction in sequential supervised learning.” 2009. Doctoral Dissertation, Oregon State University. Accessed January 17, 2021. http://hdl.handle.net/1957/12548.

MLA Handbook (7^th Edition):

Hao, Guohua. “Efficient training and feature induction in sequential supervised learning.” 2009. Web. 17 Jan 2021.

Vancouver:

Hao G. Efficient training and feature induction in sequential supervised learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/1957/12548.

Council of Science Editors:

Hao G. Efficient training and feature induction in sequential supervised learning. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/12548

Linköping University

28. Alirezaie, Marjan. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.

Degree: Computer and Information Science, 2011, Linköping University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086

► The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem… (more)

Subjects/Keywords: Machine Learning; Supervised Learning; Unsupervised Learning; Computer Sciences; Datavetenskap (datalogi)

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APA (6^th Edition):

Alirezaie, M. (2011). Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Alirezaie, Marjan. “Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.” 2011. Thesis, Linköping University. Accessed January 17, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Alirezaie, Marjan. “Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.” 2011. Web. 17 Jan 2021.

Vancouver:

Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Internet] [Thesis]. Linköping University; 2011. [cited 2021 Jan 17]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Thesis]. Linköping University; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

29. Jurasiński, Karol (author). Towards deeper understanding of semi-supervised learning with variational autoencoders.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb

► Recently, deep generative models have been shown to achieve state-of-the-art performance on semi-supervised learning tasks. In particular, variational autoencoders have been adopted to use labeled… (more)

Subjects/Keywords: semi-supervised learning; variational inference; deep learning; machine learning

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APA (6^th Edition):

Jurasiński, K. (. (2019). Towards deeper understanding of semi-supervised learning with variational autoencoders. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb

Chicago Manual of Style (16^th Edition):

Jurasiński, Karol (author). “Towards deeper understanding of semi-supervised learning with variational autoencoders.” 2019. Masters Thesis, Delft University of Technology. Accessed January 17, 2021. http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb.

MLA Handbook (7^th Edition):

Jurasiński, Karol (author). “Towards deeper understanding of semi-supervised learning with variational autoencoders.” 2019. Web. 17 Jan 2021.

Vancouver:

Jurasiński K(. Towards deeper understanding of semi-supervised learning with variational autoencoders. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 17]. Available from: http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb.

Council of Science Editors:

Jurasiński K(. Towards deeper understanding of semi-supervised learning with variational autoencoders. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb

30. WALLHEDE, ERIK. Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch .

Degree: Chalmers tekniska högskola / Institutionen för data och informationsteknik, 2019, Chalmers University of Technology

URL: http://hdl.handle.net/20.500.12380/301903

► Artificial intelligence research is currently a hot topic within many industries. In terms of research, games such as StarCraft II provide a good testing ground… (more)

Subjects/Keywords: Artificial Neural Networks; Machine Learning; StarCraft II; Reinforcement learning; Supervised learning

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APA (6^th Edition):

WALLHEDE, E. (2019). Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/301903

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

WALLHEDE, ERIK. “Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch .” 2019. Thesis, Chalmers University of Technology. Accessed January 17, 2021. http://hdl.handle.net/20.500.12380/301903.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

WALLHEDE, ERIK. “Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch .” 2019. Web. 17 Jan 2021.

Vancouver:

WALLHEDE E. Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch . [Internet] [Thesis]. Chalmers University of Technology; 2019. [cited 2021 Jan 17]. Available from: http://hdl.handle.net/20.500.12380/301903.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

WALLHEDE E. Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch . [Thesis]. Chalmers University of Technology; 2019. Available from: http://hdl.handle.net/20.500.12380/301903

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Open Access Theses and Dissertations